Modeling
| Citation | Link | Keywords |
| Zitzmann C and Kaderali L (2018) Mathematical Analysis of Viral Replication Dynamics and Antiviral Treatment Strategies: From Basic Models to Age-Based Multi-Scale Modeling. Front. Microbiol. 9:1546. doi: 10.3389/fmicb.2018.01546 | https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.01546/full | mathematical modeling, viral kinetics, viral replication, human immunodeficiency virus, Hepatitis C virus, Influenza A virus, antiviral therapy, immune response |
| Almansour S, Dunster JL, Crofts JJ, Nelson MR (2024) Modelling the continuum of macrophage phenotypes and their role in inflammation, Mathematical Biosciences, Volume 377, 109289, ISSN 0025-5564, https://doi.org/10.1016/j.mbs.2024.109289. |
https://www.sciencedirect.com/science/article/pii/S0025556424001494 | mathematical modeling, macrophages and inflammation, Bifurcation analysis, PDE |
| Chathoth K, Fostier L, Martin B, Baysse C, Mahé F (2022) A Multi-Skilled Mathematical Model of Bacterial Attachment in Initiation of Biofilms. Microorganisms,10(4):686. https://doi.org/10.3390/microorganisms10040686 | https://www.mdpi.com/2076-2607/10/4/686 | biofilm, bacterial attachment, mathematical model, stochastic, 2D and 3D |
| Schmid N, Fernandes Del Pozo D, Waegeman W, Hasenauer J (2025) Assessment of uncertainty quantification in universal differential equations. Philos Trans A Math Phys Eng Sci; 383(2293):20240444. doi:10.1098/rsta.2024.0444 | https://pubmed.ncbi.nlm.nih.gov/40172556/ | uncertainty quantification, universal differential equations, scientific machine learning |
| Maddu SCheeseman BLSbalzarini IFMüller CL (2022) Stability selection enables robust learning of differential equations from limited noisy data. Proc. A; 478 (2262): 20210916. https://doi.org/10.1098/rspa.2021.0916 | https://royalsocietypublishing.org/rspa/article/478/2262/20210916/54488/Stability-selection-enables-robust-learning-of | stability selection, sparse regression, PDE identification |
| Heinrich V, Simpson WD 3rd, Francis EA (2017) Analytical Prediction of the Spatiotemporal Distribution of Chemoattractants around Their Source: Theory and Application to Complement-Mediated Chemotaxis. Front Immunol.; 8:578. Published 2017 May 26. doi:10.3389/fimmu.2017.00578 | https://pmc.ncbi.nlm.nih.gov/articles/PMC5445147/ | chemotaxis, reaction–diffusion, mathematical model, single-cell, host–pathogen |
| Niemann J-H, Klus S, Schütte C (2021) Data-driven model reduction of agent-based systems using the Koopman generator. PLoS ONE 16(5): e0250970. https://doi.org/10.1371/journal.pone.0250970 | https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0250970 | ABM, PDEs, data-driven reduction |
| Lorenzi TPainter KJ (2025) Pattern formation within phenotype-structured chemotactic populations. Proc. A 1; 481 (2324): 20250483. https://doi.org/10.1098/rspa.2025.0483 | https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2025.0483 | PDEs, pattern formation, chemotaxis, non-local advection-diffusion-reaction eqs. |
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Kejie C, Kai-Rong O (2021) Random Walks of a Cell With Correlated Speed and Persistence Influenced by the Extracellular Topography, Frontiers in Physics, Volume 9, 10.3389/fphy.2021.719293 |
https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.719293/full | Random walks, complex environments, PRWs, Cell migration |
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Ohno K, Kobayashi Y, Uesaka M et al. (2021) A computational model of the epidermis with the deformable dermis and its application to skin diseases. Sci Rep 11, 13234. https://doi.org/10.1038/s41598-021-92540-1 |
https://www.nature.com/articles/s41598-021-92540-1 | ABM, skin modelling, skin disease, cellular layer |
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